Anomaly localization in a pipeline

ABSTRACT

A method for locating an anomaly in a fluid transmission pipeline system is provided. The method may include receiving data for one or more physical conditions measured at an input and output of a pipeline portion, performing multiple simulations on a model of the pipeline portion to determine sets of simulated conditions that respectively correspond to simulated leak locations, determining a probability for a leak at one or more of the simulated leak locations by comparing one or more sets of simulated conditions to the received output data, and determining a highest probability location for the leak based on the probability for the leak at the one or more of the simulated leak locations. At least one simulation of the multiple simulations may be performed as a stochastic process and may be based on the received input data.

BACKGROUND

The present invention generally relates to anomaly localization in apipeline, and more particularly locating an anomaly in a fluidtransmission pipeline system.

Fluids, such as compressible gas, may be transmitted by pipeline. Forexample, natural gas may be transmitted by a pipeline via a highpressure gas transmission pipeline system. A leak (or an anomaly) in apipeline may lead to a catastrophic rupture event in the pipeline. Aleak may be detected by a monitoring system, e.g., a supervisory controland data acquisition (SCADA) system. However, leaks (or anomalies) inthe pipeline system may be difficult to identify and locate. Sensorsdesigned to measure certain physical conditions within a pipelinesection may be biased and noisy. Moreover, such sensors may be situatedat stations within the pipeline system that may be topologicallyconnected, but separated from each other by distances from a few milesto about 50 miles.

SUMMARY

According to one embodiment, a method for locating an anomaly in a fluidtransmission pipeline system is provided. The method may includereceiving input and output data for one or more physical conditionsrespectively measured at an input and output of a pipeline portion,performing multiple simulations on a model of the pipeline portion todetermine sets of simulated conditions that respectively correspond tosimulated leak locations, determining a probability for a leak at one ormore of the simulated leak locations by comparing one or more sets ofsimulated conditions to the output data, and determining a highestprobability location for the leak based on the probability for the leakat the one or more of the simulated leak locations. At least onesimulation of the multiple simulations may be performed as a stochasticprocess and may be based on the input data.

According to another embodiment, a computer program product for locatingan anomaly in a fluid transmission pipeline system is provided. Thecomputer program product may include at least one computer readablenon-transitory storage medium having computer readable programinstructions for execution by a processor. The computer readable programinstructions may include instructions for receiving input and outputdata for one or more physical conditions respectively measured at aninput and output of a pipeline portion, performing multiple simulationson a model of the pipeline portion to determine sets of simulatedconditions that respectively correspond to simulated leak locations,determining a probability for a leak at one or more of the simulatedleak locations by comparing one or more sets of simulated conditions tothe output data, and determining a highest probability location for theleak based on the probability for the leak at the one or more of thesimulated leak locations. At least one simulation of the multiplesimulations may be performed as a stochastic process and may be based onthe input data.

According to another embodiment, a computer system for locating ananomaly in a fluid transmission pipeline system is provided. The systemmay include at least one processing unit, at least one computer readablememory, at least one computer readable tangible, non-transitory storagemedium, and program instructions stored on the at least one computerreadable tangible, non-transitory storage medium for execution by the atleast one processing unit via the at least one computer readable memory.The program instructions may include instructions for receiving inputand output data for one or more physical conditions respectivelymeasured at an input and output of a pipeline portion, performingmultiple simulations on a model of the pipeline portion to determinesets of simulated conditions that respectively correspond to simulatedleak locations, determining a probability for a leak at one or more ofthe simulated leak locations by comparing one or more sets of simulatedconditions to the output data, and determining a highest probabilitylocation for the leak based on the probability for the leak at the oneor more of the simulated leak locations. At least one simulation of themultiple simulations may be performed as a stochastic process and may bebased on the input data.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description, given by way of example and notintended to limit the invention solely thereto, will best be appreciatedin conjunction with the accompanying drawings, in which:

FIG. 1 is a composite diagram illustrating a leak along a portion of apipeline and a graph representing an internal pressure along the portionof the pipeline, according to an embodiment;

FIG. 2 is a flowchart illustrating an exemplary method for locating ananomaly in a fluid transmission pipeline system, according to anembodiment;

FIG. 3 is a diagram illustrating a leak along a section of a pipeline,according to an embodiment;

FIG. 4 is a block diagram illustrating an exemplary method for locatingan anomaly in a fluid transmission pipeline system, according to anembodiment;

FIG. 5 is another flowchart illustrating an exemplary method forlocating an anomaly in a fluid transmission pipeline system, accordingto an embodiment; and

FIG. 6 is a block diagram illustrating a computing node, according to anaspect of the invention.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention. In the drawings, like numbering representslike elements.

DETAILED DESCRIPTION

Various embodiments of the present invention will now be discussed withreference to FIGS. 1-6, like numerals being used for like andcorresponding parts of the various drawings.

According to one embodiment, provided is a method for locating ananomaly (e.g., a leak) in a fluid transmission pipeline system byreceiving input data and output data for one or more physical conditions(e.g., flowrate, pressure, temperature) respectively measured at aninput and output of a portion of a pipeline, determining a plurality ofsets of conditions (i.e., simulated conditions) respectivelycorresponding to a plurality of simulated leaks, and comparing thesimulated conditions to the measured physical conditions to determine amost-likely location of a leak in the portion of the pipeline.

According to a further embodiment, simulated conditions (i.e., a set ofsimulated conditions) resulting from an assumed leak at a particularlocation (i.e., a simulated leak location) may be determined byperforming a simulation on a model of the portion of the pipeline.Different simulated leak locations may be tested in multiplesimulations, and corresponding conditions resulting from each of thedifferent simulated leak locations may be determined. Probabilities forleaks at the simulated leak locations may be determined by comparingsets of simulated conditions to the received output data, and a highestprobability location for the leak may be determined from theprobabilities for leaks at the simulated leak locations. At least one ofthe multiple simulations may be performed as a stochastic process andmay be based on the received input data.

As described herein, a stochastic process may include a randomly orpseudo-randomly determined selection process for values used in themultiple simulations. For example, values provided to a simulator may beselected from a multitude of values (e.g., various pressure values,various flowrate values, and various assumed leak locations), and one ormore of those values may be randomly or pseudo-randomly selected. Thestochastic process may also select simulator values from live,incoming/received input data. For example, a temporal measurement ofpressure at an input of a pipeline portion may be used in a particularsimulation. The stochastic process may also compare simulation results(e.g., sets of simulated conditions) to live, incoming/received outputdata. For example, a temporal measurement of pressure at an output of apipeline portion may be used to determine a probability of a leak at aparticular location.

In an embodiment, a sufficient number of simulations may be performed toobtain enough leak location probabilities to provide an adequatedistribution of probabilities prior to reaching a particular confidencelevel regarding the location probability for a leak.

According to one embodiment, the highest probability location for theleak may further be based on stochastically updating the probabilitiesfor leaks at the simulated leak locations, and the one or more updatedprobabilities may be based on stochastically selected input data. In afurther embodiment, the one or more updated probabilities may further bebased on a stochastically selected simulated leak location.

The methods, computer program products, and systems disclosed herein mayprovide enhanced leak localization that accounts for uncertainty (e.g.,noisy measurements) and sensor bias by continuously and dynamicallyupdating numerical probability simulations in a stochastic fashion. Thestochastic updating may use live, incoming/received input and outputdata, and/or randomly or pseudo-randomly generated simulations (e.g.,different configurations, simulated leak locations, simulated leaksizes, etc.).

The methods, computer program products, and systems disclosed herein mayenable real time (or near real time) detection of leaks that may allowprevention of rupture events. Leak location information may provideimportant information for maintenance crews to address a weak spot in apipeline and ultimately prevent a potentially catastrophic rupture eventin the pipeline.

FIG. 1 is a composite diagram 100 illustrating a portion of pipeline 102with a leak 108 and a pressure graph 112 representing an internalpressure along the portion of pipeline 102. The portion of pipeline 102may transmit a fluid, e.g., a compressible gas under high pressure. Theportion of pipeline 102 may have an input 104 (where the fluid entersthe portion of pipeline 102) and an output 106 (where the fluid exitsthe portion of pipeline 102). At input 104, one or more physicalconditions may be measured, e.g., an input flowrate (F_in), an inputpressure (P_in), an input temperature (T_in). At output 106, one or morephysical conditions may be measured, e.g., an output flowrate (F_out),an output pressure (P_out), an input temperature (T_out). Measurementstaken at input 104 and output 106 may be biased or contain noise. Forexample, flowrate measurements may be highly biased and noisy relativeto pressure measurements. In other examples, sensors (used to take themeasurements) may introduce bias or noise.

Pressure graph 112 represents an exemplary internal pressure along theportion of pipeline 102. The internal pressure (P) is plotted as afunction of distance (Y_(pipe)) along the portion of pipeline 102.Pressure graph 112 illustrates an exemplary increase in pressureproximate to the location of leak 108 (at Y_(leak)).

FIG. 2 illustrates a first flowchart 200 depicting an exemplary methodfor locating an anomaly in a fluid transmission pipeline system,according to an embodiment. At 202, data may be received from a fluidtransmission pipeline system. The data may include temporal measurementsof one or more physical conditions (e.g., flowrate, pressure,temperature). The one or more physical conditions may be measured at aninput of a section of a pipeline and an output of the section of thepipeline.

The section of the pipeline may be a portion of the pipeline situatedbetween two topologically connected stations (e.g., compressorstations). For example, the input of the pipeline section may be locatedat a first station and the output of the pipeline section may be locatedat a second station. In an embodiment, sensors (that measure physicalconditions) may be located at the input of the pipeline section (e.g.,at the first station) and at the output of the pipeline section (e.g.,at the second station). It will be appreciated that input and outputsensors need not be situated at a pipeline station, and may be situatedbetween topologically connected stations. It will be further appreciatedthat a measurement sensor proximate to an input portion/section of apipeline may be considered an input sensor, and a measurement sensorproximate to an output portion/section of the pipeline may be consideredan output sensor.

The fluid transmission pipeline system (i.e., a transmission network)may include one or more subsystems. A subsystem may define a subsectionof the transmission network. A subsystem may include one or moresections of a pipeline in the transmission network. Data (e.g., temporalmeasurements of physical conditions) may be received from one or moresubsystems in the fluid transmission pipeline system.

At 204, a computational fluid dynamics model (CFD model) of a pipelinesection may be generated. The CFD model may be based on athree-dimensional geometric model (3D model) for the pipeline section,which may be constructed from pipe property data and geospatialinformation. The 3D model may be discretized into finite elements usinga meshing algorithm.

The CFD model may be based on a transient numerical simulation model forthe fluid (e.g., high pressure gas) inside the pipeline section. Inputvariables for the transient numerical simulation model may includetime-varying inlet pressure (i.e., input pressure), flowrate, and leaklocation (i.e., simulated leak location).

The CFD model may account for two sub-regions within a flow regionoutside an assumed leak location: a jet flow region and a turbulenceflow region. Referring now to FIG. 3, a diagram of a pipeline section300 with leak 302 is provided. Fluid flowing from leak 302 may formfluid flow region 304. Fluid flow region 304 may include a jet flowregion 306 and a turbulence flow region 308. The x-axis represents adistance along the pipeline section (i.e., simulated leak location) andthe y-axis represents a perpendicular distance from the pipeline sectionalong the leak flow. Similarly, “u” represents a horizontal fluidvelocity and “v” represents a vertical fluid velocity.

The jet flow region may be modeled with jet flow equations (e.g.,two-dimensional laminar jet flow equations). The jet flow equations maybe used to calculate boundary conditions of flowrate at the leaklocation as a function of pressure and leak size. Exemplary jet flowequations may include:

$\begin{matrix}{{{\frac{\partial u}{\partial x} + \frac{\partial v}{\partial y}} = 0},} & {{Equation}\mspace{14mu} 1} \\{{{{u\frac{\partial u}{\partial x}} + {v\frac{\partial u}{\partial y}}} = {{- {\nabla p}} + {\mu \frac{\partial^{2}u}{\partial^{2}y}}}},} & {{Equation}\mspace{14mu} 2} \\{\left. u_{\max}^{2} \right.\sim{- {{\nabla p}.}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In the above equations x and y are, respectively, a horizontal dimensionalong the pipeline section and a vertical dimension perpendicular to thepipeline, u and v are, respectively, horizontal and vertical fluidvelocities, p is pressure, and μ is fluid viscosity. Equation 3illustrates a relationship where stochastic variables associated withhorizontal fluid velocity (u_(max) ²) and pressure (−∇p) may have thesame statistical distributions.

The turbulence flow region may be modeled with a standard k-ε turbulencemodel for compressible flow and Navier-Stokes equations. Exemplaryequations may include:

$\begin{matrix}{{{\frac{\partial\rho}{\partial t} + \frac{{\partial\rho}\; u_{j}}{\partial x_{j}}} = 0},} & {{Equation}\mspace{14mu} 4} \\{{{{\frac{\partial}{\partial t}\rho \; u_{i}} + {\frac{\partial}{\partial x_{j}}\left\lbrack {{\rho \; u_{i}u_{j}} + {p\; \delta_{ij}} - \tau_{ij}} \right\rbrack}} = 0},{i = 1},2,3,} & {{Equation}\mspace{14mu} 5} \\{{{\frac{\partial{\rho ɛ}}{\partial t} + \frac{{\partial\rho}\; {ku}_{i}}{\partial x_{i}}} = {{\frac{\partial}{\partial x_{j}}\left\lbrack {\left( {\mu + \frac{\mu_{t}}{\sigma_{k}}} \right)\frac{\partial k}{\partial x_{j}}} \right\rbrack} + {{\mu_{t}\left( {\frac{\partial u_{j}}{\partial x_{i}} + \frac{\partial u_{i}}{\partial x_{j}}} \right)}\frac{\partial u_{i}}{\partial x_{j}}} - {\beta \; g_{i}\frac{\mu_{t}}{\Pr_{t}}\frac{\partial T}{\partial x_{i}}} - ɛ}},} & {{Equation}\mspace{14mu} 6} \\{{u_{j}\frac{\partial ɛ}{\partial x_{j}}} = {{\frac{\partial}{\partial x_{j}}\left\lbrack {\left( {\mu - \frac{\mu_{t}}{\sigma_{ɛ}}} \right)\frac{\partial ɛ}{\partial x_{j}}} \right\rbrack} + {\frac{ɛ}{k}\left\lbrack {C_{1}{\mu_{t}\left( {\frac{\partial u_{j}}{\partial x_{i}} + \frac{\partial u_{i}}{\partial x_{j}}} \right)}\frac{\partial u_{i}}{\partial x_{j}}} \right\rbrack} - {C_{2}\frac{ɛ^{2}}{k}} + {C_{3}\frac{ɛ}{k}\beta \; g_{i}\frac{\mu_{t}}{\Pr_{t}}{\frac{\partial T}{\partial x_{i}}.}}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

Equation 4 represents a mass formula, where ρ is density and u is fluidvelocity. Equation 5 represents a momentum formula, where τ is adeviatoric stress tensor. Equations 6 and 7, respectively, representformulas for turbulent kinetic energy and a dissipation rate ofturbulent kinetic energy, where μ is molecular viscosity and μ_(t) isturbulent viscosity, k is turbulent kinetic energy, and ε is dissipationrate of k.

Referring back to FIG. 2, at 206, leaks may be simulated in the CFDmodel of the pipeline section. The CFD model may determine a set ofsimulated conditions that may reflect one or more (simulated) physicalconditions at an output of the pipeline section resulting from anassumed leak. Multiple simulations may be performed with different inputvariables (e.g., various pressures, flowrates, temperatures) and varyingassumptions for the assumed leak (e.g., various simulated leaklocations, leak sizes). As a result, multiple sets of simulatedconditions may be generated that respectively correspond to particularinput variables and particular leak assumptions. In an embodiment, thedifferent input configurations (e.g., varying input variables andvarying leak assumptions) for the CFD simulations may be stochasticallydetermined. In an embodiment, input variables may be stochasticallyselected from input data obtained in 202 (above).

At 208, probabilities for leaks at the simulated locations may bedetermined based on a comparison between the one or more physicalconditions measured at the output of the pipeline section and themultiple sets of simulated conditions (generated by the leak simulationperformed in 206, above). The probabilities may be based on a pluralityof comparisons. For example, a first set of simulated conditions maymatch closer to measured physical conditions than a second set ofsimulated conditions, but a third set of simulated conditions may matchcloser than the first set and so on. As such, certain sets of simulatedconditions among the multiple sets may have higher probabilities thanother sets of simulated conditions from the multiple sets.

In a further example, if a leak is present in a pipeline section, aparticular set of simulated conditions that most closely matches actualoutput measurements may determine a most likely leak location. In otherwords, the simulated leak location corresponding to that particular setof simulated conditions may be the most likely leak location. It will beappreciated that a level of confidence (e.g., for the most likely leaklocation) may be based on the amount of simulations performed andcomparisons made. The level of confidence may be increased bystochastically performing multiple simulations and comparisons.

At 210, the probabilities for leaks at the simulated locations may bedynamically updated by stochastically performing additional CFDsimulations with different (e.g., varying) input configurations and leakassumptions. In an embodiment, the dynamic updating may be based on liveor temporal incoming data for one or more physical conditions measuredat the input of the pipeline section. In an embodiment, the dynamicupdating may be based on live or temporal incoming data for one or morephysical conditions measured at the output of the pipeline section. Inan embodiment, the dynamic updating may be based on stochasticallyselecting input configurations and/or leak assumptions.

FIG. 4 illustrates a block diagram 400 depicting an exemplary method forlocating an anomaly in a fluid transmission pipeline system, accordingto an embodiment. Incoming data 402 (e.g., one or more physicalconditions measured at an input and/or output of a pipeline section) maybe received.

At initial phase 404, incoming data 402 may be pre-processed forsubsequent processing or analysis including anomaly localizationestimation and generation of an optimized output model for anomalylocalization. For example, measurement data 404A may be extracted fromthe incoming data 402 by performing various pre-process analyses (e.g.,time series analytics) on the incoming data 402. Pre-process analysesmay include removal of data outliers or short spikes in the data.

Pre-processing of measurement data 404A may be based on a particularcharacteristic for which the one or more physical conditions may becompared (e.g., matched) to the simulated conditions (generated by theCFD model simulations). A particular characteristic may includecharacteristic/moment generating functions of random variables,percentiles of random variables, or temporal correlations.Pre-processing may also be based on conditional metrics, e.g., theconditional distribution of the one or more measured physicalconditions.

Also, at initial phase 404, numerical simulator 404B may be generated.In an embodiment, numerical simulator 404B may be the CFD model for thepipeline section, as described above. In an embodiment, numericalsimulator 404B may be a program run as an inner loop of the overallmethod for locating an anomaly in a fluid transmission pipeline system.

Further, at initial phase 404, previously determined probabilities 404C(i.e., probabilities associated with prior simulated or detected leaks)may be received and provided as input for further processing. Previouslydetermined probabilities 404C may include information taken from otherpipeline detection methods (e.g., big leak detection model, leaklocalization based on time of travel methodology) and/or previous outputmodels generated by the methods disclosed herein (e.g., model output408).

At estimation phase 406, measurement data 404A, data from numericalsimulator 404B, and/or previously determined probabilities 404C, may bereceived as inputs for anomaly localization estimation. Anomalylocalization estimation may include running simulations on the CFD model(e.g., numerical simulator 404B) multiple times under differentconfigurations (e.g., varying input variables, varying leak assumptions)to generate multiple sets of simulated conditions. As described above,each set of simulated conditions may correspond to a particularsimulated leak location. Anomaly localization estimation may alsoinclude comparing each of the multiple sets of simulated conditions toincoming/measured output conditions (i.e., one or more physicalconditions measured at the output of the pipeline section) to determinea probability of a leak at each simulated leak location.

Probabilities of leaks at the simulated leak locations may be optimizedby running further simulations on the CFD model. Such furthersimulations may be stochastically determined (e.g., variables andconfigurations may be randomly or pseudo-randomly selected, and/orselected to satisfy a desired statistical distribution). Such furthersimulations may also be used to fine-tune the confidence level of theprobabilities for leaks at simulated leak locations.

Model output 408 may be generated based on the anomaly localizationestimation. Model output 408 may include one or more probabilities for aleak at one or more of the simulated leak locations. In an embodiment,model output 408 may include a most likely location (i.e., highestprobability location) for a leak based on the probabilities for leaks atthe simulated locations. In an embodiment, model output 408 may includea distribution of probabilities for a leak at a particular locationamong a plurality of simulated leak locations.

In an embodiment, information (e.g., probabilities, configurations) frommodel output 408 may be used in a subsequent leak localizationdetermination by providing the information to the initial phase 404(e.g., as part of previously determined probabilities 404C).

FIG. 5 illustrates a second flowchart 500 depicting an exemplary methodfor locating an anomaly in a fluid transmission pipeline system,according to an embodiment. At 502, input data and output data for oneor more physical conditions in a portion of a pipeline (i.e., a pipelinesection) may be received. The input data may be measured at an input ofthe pipeline section and the output data may be measured at an output ofthe pipeline section. The one or more physical conditions may includeflowrate, pressure, or temperature.

At 504, multiple simulations on a model of the pipeline section may beperformed to determine sets of simulated conditions corresponding tosimulated leak locations. The model of the pipeline section may be a CFDmodel based on a 3D model for the pipeline section and may further bebased on a transient numerical simulation model for fluid (e.g.,compressible, high pressure gas) inside the pipeline section. Eachsimulation from the multiple simulations may determine a set ofsimulated conditions that corresponds to a particular simulated leaklocation. The multiple simulations may determine a plurality of sets ofsimulated conditions that respectively correspond to a plurality ofsimulated leak locations. In an embodiment, sets of simulated conditionsmay further respectively correspond to other simulated leak assumptionssuch as leak size.

In an embodiment, at least one simulation of the multiple simulationsmay be performed as a stochastic process and may be based on the inputdata. The stochastic process may include randomly or pseudo-randomlygenerated simulations (e.g., different configurations, simulated leaklocations, simulated leak sizes, etc.), and/or randomly orpseudo-randomly selected incoming/received input data. In an embodiment,the multiple simulations may have one or more aspects (e.g., aparticular variable or configuration) representing a desired statisticaldistribution among the multiple simulations.

At 506, probabilities for leaks at the simulated leak locations may bedetermined by comparing the sets of simulated conditions to the outputdata (i.e., output data measured at the output of the pipeline section).In an embodiment, a probability for a leak at one or more simulated leaklocations from the plurality of simulated leak locations may bedetermined by comparing one or more sets of simulated conditions fromthe plurality of sets of simulated conditions to the output data.

At 508, a highest probability location for a leak may be determinedbased on the probabilities for leaks at the simulated leak locations. Inan embodiment, a highest probability location for the leak may bedetermined based on the probability for the leak at one or moresimulated leak locations from the plurality of simulated leak locations.

The methods, computer products, and systems disclosed herein mayidentify a most likely location (i.e., a highest probability location)for a leak within a pipeline section in a fluid transmission pipelinesystem based on stochastically performed simulations on a model of thepipeline section, and may further account for compressible flow withinthe pipeline section (e.g., associated with high pressure gastransmission). Localization of a leak may be determined in real time (ornear real time) by stochastically and dynamically updating probabilityestimates and/or updating simulations performed on a model of a pipelinesection. Stochastic and dynamic updates may be based on live, incomingdata (e.g., measurements of one or more physical conditions).

It is contemplated that real time (or near real time) localization of aleak may be associated with an alarm indicating that a leak hasoccurred. It is further contemplated that the methods disclosed hereinmay be incorporated into a leak detection system, and may furtherprovide a real time (or near real time) leak alert by continuously (andstochastically) comparing live, incoming pipeline data to sets ofsimulated conditions (corresponding to assumed leaks). For example, analert may be triggered when live, incoming output data matches a set ofsimulated conditions above a certain confidence level (e.g., adetermined probability of a leak at a simulated location exceeds apredetermined threshold). It will be appreciated that, as disclosedherein, such an alert may also include the location of the assumed leakcorresponding to the matching set of simulated conditions.

In one embodiment, determining the highest probability location for theleak is further based on stochastically updating the probability for theleak at one or more simulated leak locations, and the one or moreupdated probabilities are based on stochastically selected input data.In a further embodiment, the one or more updated probabilities arefurther based on a stochastically selected simulated leak location.

In one embodiment, the one or more physical conditions includes at leastone of a flow rate, a pressure, or a temperature.

In one embodiment, the fluid transmission pipeline system transmits acompressible fluid. In a further embodiment, the fluid transmissionpipeline system is a high pressure gas transmission pipeline system.

In one embodiment, one or more simulations from the multiple simulationsis further based on data from a previous simulation on the model of theportion of the pipeline.

In one embodiment, the model of the portion of the pipeline is atransient numerical simulation model based on flowrate, pressure, andthe plurality of simulated leak locations. In a further embodiment, thetransient numerical simulation model is further based on athree-dimension geometrical model of the portion of the pipeline. Inanother further embodiment, one or more simulations from the multiplesimulations is further based on data from another model associated withthe portion of the pipeline.

Embodiments disclosed and contemplated herein may be implemented and/orperformed by any type of computer, known or contemplated, regardless ofthe platform being suitable for storing and/or executing program code.

FIG. 6 depicts a schematic illustrating an example of a computing node.Computing node 10 is only one example of a suitable computing node andis not intended to suggest any limitation as to the scope of use orfunctionality of embodiments of the invention described herein.Regardless, computing node 10 is capable of being implemented and/orperforming any of the functionality set forth hereinabove.

In computing node 10 there is a computer system/server 12, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed computing environments that includeany of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inboth local and remote computer system storage media including memorystorage devices.

As shown in FIG. 6, computer system/server 12 in computing node 10 isshown in the form of a general-purpose computing device. The componentsof computer system/server 12 may include, but are not limited to, one ormore processors or processing units 16, a system memory 28, and a bus 18that couples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

References in the specification to “one embodiment”, “an embodiment”,“an example embodiment”, etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A method for locating an anomaly in a fluidtransmission pipeline system, the method comprising: receiving inputdata and output data for one or more physical conditions, wherein theinput data is measured at an input of a portion of a pipeline and theoutput data is measured at an output of the portion of the pipeline, andwherein the one or more physical conditions comprise at least one of aflowrate, a pressure, or a temperature; performing multiple simulationson a model of the portion of the pipeline, wherein each simulationdetermines a set of simulated conditions that corresponds to a simulatedleak location, and wherein the multiple simulations determine aplurality of sets of simulated conditions that respectively correspondto a plurality of simulated leak locations; determining a probabilityfor a leak at one or more simulated leak locations from the plurality ofsimulated leak locations by comparing one or more sets from theplurality of sets of simulated conditions to the output data, whereindetermining the highest probability location for the leak is furtherbased on stochastically updating the probability for the leak at the oneor more simulated leak locations, and wherein the one or more updatedprobabilities are based on stochastically selected input data; anddetermining a highest probability location for the leak based on theprobability for the leak at one or more simulated leak locations fromthe plurality of simulated leak locations, wherein at least onesimulation of the multiple simulations is performed as a stochasticprocess and is based on the input data.